In this paper we present steps towards the automatic detection of vulnerable road users in video. Such a system can e.g. be used as an automatic blind spot camera for trucks. The aim of the system is to automatically warn the driver when the algorithm detects vulnerable road users in the camera images. Such an application implies severe time and performance constraints on the vision system, which introduce several challenges that need to be tackled. In this paper we described a first step towards such an intelligent detection system. We compare two different pedestrian tracking algorithms that we developed and implemented, with respect to the detection results and the applicability towards real-time performance. Both algorithms start from the same appearance-based pedestrian detector, and are based on a tracking-by-detection approach. The first algorithm uses the appearance-based detector in a Kalman filter motion model framework. The second algorithm uses the probablistic detection result in a particle filter framework, to enable the use of multiple tracking hypothesis. We recorded two datasets: one with a standard camera from a moving vehicle and one recorded with a real blind-spot camera. At the moment evaluation of the algorithms is performed on the first dataset. To achieve real-time performance we implemented part of the appearance-based pedestrian detector onto dedicated GPU hardware using OpenCL. The speedup results that we achieved will also be discussed.

Email address protected by JavaScript. Activate javascript to see the email.

We use cookies to improve our service for you. You can find more information in our data protection declaration. By continuing to use our site, you accept our use of cookies and Privacy Policy.OkPrivacy policy